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Article: SMART: A subspace clustering algorithm that automatically identifies the appropriate number of clusters

TitleSMART: A subspace clustering algorithm that automatically identifies the appropriate number of clusters
Authors
KeywordsWeighting
Subspace clustering
κ-means
Cluster numbers
Data mining
Issue Date2009
Citation
International Journal of Data Mining, Modelling and Management, 2009, v. 1, n. 2, p. 149-177 How to Cite?
AbstractThis paper presents a subspace κ-means clustering algorithm for high-dimensional data with automatic selection of κ. A new penalty term is introduced to the objective function of the fuzzy κ-means clustering process to enable several clusters to compete for objects, which leads to merging some cluster centres and the identification of the 'true' number of clusters. The algorithm determines the number of clusters in a dataset by adjusting the penalty term factor. A subspace cluster validation index is proposed and employed to verify the subspace clustering results generated by the algorithm. The experimental results from both the synthetic and real data have demonstrated that the algorithm is effective in producing consistent clustering results and the correct number of clusters. Some real datasets are used to demonstrate how the proposed algorithm can determine interesting sub-clusters in the datasets. Copyright © 2009 Inderscience Enterprises Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/276925
ISSN
2020 SCImago Journal Rankings: 0.151
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJing, Liping-
dc.contributor.authorLi, Junjie-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorCheung, Yiu Ming-
dc.contributor.authorHuang, Joshua-
dc.date.accessioned2019-09-18T08:35:04Z-
dc.date.available2019-09-18T08:35:04Z-
dc.date.issued2009-
dc.identifier.citationInternational Journal of Data Mining, Modelling and Management, 2009, v. 1, n. 2, p. 149-177-
dc.identifier.issn1759-1163-
dc.identifier.urihttp://hdl.handle.net/10722/276925-
dc.description.abstractThis paper presents a subspace κ-means clustering algorithm for high-dimensional data with automatic selection of κ. A new penalty term is introduced to the objective function of the fuzzy κ-means clustering process to enable several clusters to compete for objects, which leads to merging some cluster centres and the identification of the 'true' number of clusters. The algorithm determines the number of clusters in a dataset by adjusting the penalty term factor. A subspace cluster validation index is proposed and employed to verify the subspace clustering results generated by the algorithm. The experimental results from both the synthetic and real data have demonstrated that the algorithm is effective in producing consistent clustering results and the correct number of clusters. Some real datasets are used to demonstrate how the proposed algorithm can determine interesting sub-clusters in the datasets. Copyright © 2009 Inderscience Enterprises Ltd.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Data Mining, Modelling and Management-
dc.subjectWeighting-
dc.subjectSubspace clustering-
dc.subjectκ-means-
dc.subjectCluster numbers-
dc.subjectData mining-
dc.titleSMART: A subspace clustering algorithm that automatically identifies the appropriate number of clusters-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJDMMM.2009.026074-
dc.identifier.scopuseid_2-s2.0-84863337579-
dc.identifier.volume1-
dc.identifier.issue2-
dc.identifier.spage149-
dc.identifier.epage177-
dc.identifier.eissn1759-1171-
dc.identifier.isiWOS:000219565500002-
dc.identifier.issnl1759-1171-

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